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Various Flux documentation fixes (#1867) 

* closes influxdata/DAR#145
* added info about pivot dropping columns not in the group key, resolves #1836
* added column param to last function, closes #1823, closes influxdata/flux#3316
* closes #1707
* closes #1672
* closes #1650
pull/1873/head
Scott Anderson 2020-11-19 12:56:30 -07:00 committed by GitHub
parent c0bcee3342
commit 86fe195c4d
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89
content/influxdb/cloud/reference/flux/stdlib/built-in/transformations/aggregates/quantile.md
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@ -15,91 +15,4 @@ related:
- /{{< latest "influxdb" "v1" >}}/query_language/functions/#percentile, InfluxQL – PERCENTILE()
---
The `quantile()` function returns records from an input table with `_value`s that fall within
a specified quantile or it returns the record with the `_value` that represents the specified quantile.
Which it returns depends on the [method](#method) used.
`quantile()` supports columns with float values.
_**Function type:** Aggregate or Selector_
_**Output data type:** Float | Record_
```js
quantile(
column: "_value",
q: 0.99,
method: "estimate_tdigest",
compression: 1000.0
)
```
When using the `estimate_tdigest` or `exact_mean` methods, it outputs non-null
records with values that fall within the specified quantile.
When using the `exact_selector` method, it outputs the non-null record with the
value that represents the specified quantile.
## Parameters
### column
The column to use to compute the quantile.
Defaults to `"_value"`.
_**Data type:** String_
### q
A value between 0 and 1 indicating the desired quantile.
_**Data type:** Float_
### method
Defines the method of computation.
_**Data type:** String_
The available options are:
##### estimate_tdigest
An aggregate method that uses a [t-digest data structure](https://github.com/tdunning/t-digest)
to compute an accurate quantile estimate on large data sources.
##### exact_mean
An aggregate method that takes the average of the two points closest to the quantile value.
##### exact_selector
A selector method that returns the data point for which at least `q` points are less than.
### compression
Indicates how many centroids to use when compressing the dataset.
A larger number produces a more accurate result at the cost of increased memory requirements.
Defaults to `1000.0`.
_**Data type:** Float_
## Examples
###### Quantile as an aggregate
```js
from(bucket: "example-bucket")
|> range(start: -5m)
|> filter(fn: (r) =>
r._measurement == "cpu" and
r._field == "usage_system")
|> quantile(
q: 0.99,
method: "estimate_tdigest",
compression: 1000.0
)
```
###### Quantile as a selector
```js
from(bucket: "example-bucket")
|> range(start: -5m)
|> filter(fn: (r) =>
r._measurement == "cpu" and
r._field == "usage_system")
|> quantile(
q: 0.99,
method: "exact_selector"
)
```
{{< duplicate-oss >}}

3
content/influxdb/cloud/reference/flux/stdlib/built-in/transformations/pivot.md
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@ -41,7 +41,8 @@ The output is constructed as follows:
determined from the input tables by the value in `valueColumn` at the row identified by the
`rowKey` values and the new column's label.
If no value is found, the value is set to `null`.
- Any column that is not part of the group key or not specified in the `rowKey`,
`columnKey` and `valueColumn` parameters is dropped.
## Parameters

68
content/influxdb/cloud/reference/flux/stdlib/built-in/transformations/range.md
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@ -13,70 +13,4 @@ related:
- /{{< latest "influxdb" "v1" >}}/query_language/data_exploration/#the-where-clause, InfluxQL – WHERE
---
The `range()` function filters records based on time bounds.
Each input table's records are filtered to contain only records that exist within the time bounds.
Records with a `null` value for their time are filtered.
Each input table's group key value is modified to fit within the time bounds.
Tables where all records exists outside the time bounds are filtered entirely.
_**Function type:** Transformation_
_**Output data type:* Record_
```js
range(start: -15m, stop: now())
```
## Parameters
### start
The earliest time to include in results.
Results **include** points that match the specified start time.
Use a relative duration, absolute time, or integer (Unix timestamp in seconds).
For example, `-1h`, `2019-08-28T22:00:00Z`, or `1567029600`.
Durations are relative to `now()`.
_**Data type:** Duration | Time | Integer_
### stop
The latest time to include in results.
Results **exclude** points that match the specified stop time.
Use a relative duration, absolute time, or integer (Unix timestamp in seconds).
For example, `-1h`, `2019-08-28T22:00:00Z`, or `1567029600`.
Durations are relative to `now()`.
Defaults to `now()`.
_**Data type:** Duration | Time | Integer_
{{% note %}}
Time values in Flux must be in [RFC3339 format](/influxdb/cloud/reference/flux/language/types#timestamp-format).
{{% /note %}}
## Examples
###### Time range relative to now
```js
from(bucket:"example-bucket")
|> range(start: -12h)
// ...
```
###### Relative time range
```js
from(bucket:"example-bucket")
|> range(start: -12h, stop: -15m)
// ...
```
###### Absolute time range
```js
from(bucket:"example-bucket")
|> range(start: 2018-05-22T23:30:00Z, stop: 2018-05-23T00:00:00Z)
// ...
```
###### Absolute time range with Unix timestamps
```js
from(bucket:"example-bucket")
|> range(start: 1527031800, stop: 1527033600)
// ...
```
{{< duplicate-oss >}}

25
content/influxdb/cloud/reference/flux/stdlib/built-in/transformations/selectors/last.md
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@ -14,27 +14,4 @@ related:
- /{{< latest "influxdb" "v1" >}}/query_language/functions/#last, InfluxQL – LAST()
---
The `last()` function selects the last non-null record from an input table.
_**Function type:** Selector_
_**Output data type:** Record_
```js
last()
```
{{% warn %}}
#### Empty tables
`last()` drops empty tables.
{{% /warn %}}
## Examples
```js
from(bucket:"example-bucket")
|> range(start:-1h)
|> filter(fn: (r) =>
r._measurement == "cpu" and
r._field == "usage_system"
)
|> last()
```
{{< duplicate-oss >}}

213
content/influxdb/cloud/reference/flux/stdlib/contrib/rows/map.md
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@ -13,215 +13,4 @@ related:
- /influxdb/cloud/reference/flux/stdlib/built-in/transformations/map/
---
The `rows.map()` function is an alternate implementation of [`map()`](/influxdb/cloud/reference/flux/stdlib/built-in/transformations/map/)
that is faster, but more limited than `map()`.
`rows.map()` cannot modify [groups keys](/influxdb/cloud/reference/glossary/#group-key) and,
therefore, does not need to regroup tables.
**Attempts to change columns in the group key are ignored.**
_**Function type:** Transformation_
```js
import "contrib/jsternberg/rows"
rows.map( fn: (r) => ({_value: r._value * 100.0}))
```
## Parameters
### fn
A single argument function to apply to each record.
The return value must be a record.
_**Data type:** Function_
{{% note %}}
Use the `with` operator to preserve columns **not** in the group and **not**
explicitly mapped in the operation.
{{% /note %}}
## Examples
- [Perform mathemtical operations on column values](#perform-mathemtical-operations-on-column-values)
- [Preserve all columns in the operation](#preserve-all-columns-in-the-operation)
- [Attempt to remap columns in the group key](#attempt-to-remap-columns-in-the-group-key)
---
### Perform mathemtical operations on column values
The following example returns the square of each value in the `_value` column:
```js
import "contrib/jsternberg/rows"
data
|> rows.map(fn: (r) ({ _value: r._value * r._value }))
```
{{% note %}}
#### Important notes
The `_time` column is dropped because:
- It's not in the group key.
- It's not explicitly mapped in the operation.
- The `with` operator was not used to include existing columns.
{{% /note %}}
{{< flex >}}
{{% flex-content %}}
#### Input tables
**Group key:** `tag,_field`
| tag | _field | _time | _value |
|:--- |:------ |:----- | ------:|
| tag1 | foo | 0001 | 1.9 |
| tag1 | foo | 0002 | 2.4 |
| tag1 | foo | 0003 | 2.1 |
| tag | _field | _time | _value |
|:--- |:------ |:----- | ------:|
| tag2 | bar | 0001 | 3.1 |
| tag2 | bar | 0002 | 3.8 |
| tag2 | bar | 0003 | 1.7 |
{{% /flex-content %}}
{{% flex-content %}}
#### Output tables
**Group key:** `tag,_field`
| tag | _field | _value |
|:--- |:------ | ------:|
| tag1 | foo | 3.61 |
| tag1 | foo | 5.76 |
| tag1 | foo | 4.41 |
| tag | _field | _value |
|:--- |:------ | ------:|
| tag2 | bar | 9.61 |
| tag2 | bar | 14.44 |
| tag2 | bar | 2.89 |
{{% /flex-content %}}
{{< /flex >}}
---
### Preserve all columns in the operation
Use the `with` operator in your mapping operation to preserve all columns,
including those not in the group key, without explicitly remapping them.
```js
import "contrib/jsternberg/rows"
data
|> rows.map(fn: (r) ({ r with _value: r._value * r._value }))
```
{{% note %}}
#### Important notes
- The mapping operation remaps the `_value` column.
- The `with` operator preserves all other columns not in the group key (`_time`).
{{% /note %}}
{{< flex >}}
{{% flex-content %}}
#### Input tables
**Group key:** `tag,_field`
| tag | _field | _time | _value |
|:--- |:------ |:----- | ------:|
| tag1 | foo | 0001 | 1.9 |
| tag1 | foo | 0002 | 2.4 |
| tag1 | foo | 0003 | 2.1 |
| tag | _field | _time | _value |
|:--- |:------ |:----- | ------:|
| tag2 | bar | 0001 | 3.1 |
| tag2 | bar | 0002 | 3.8 |
| tag2 | bar | 0003 | 1.7 |
{{% /flex-content %}}
{{% flex-content %}}
#### Output tables
**Group key:** `tag,_field`
| tag | _field | _time | _value |
|:--- |:------ |:----- | ------:|
| tag1 | foo | 0001 | 3.61 |
| tag1 | foo | 0002 | 5.76 |
| tag1 | foo | 0003 | 4.41 |
| tag | _field | _time | _value |
|:--- |:------ |:----- | ------:|
| tag2 | bar | 0001 | 9.61 |
| tag2 | bar | 0002 | 14.44 |
| tag2 | bar | 0003 | 2.89 |
{{% /flex-content %}}
{{< /flex >}}
---
### Attempt to remap columns in the group key
```js
import "contrib/jsternberg/rows"
data
|> rows.map(fn: (r) ({ r with tag: "tag3" }))
```
{{% note %}}
#### Important notes
- Remapping the `tag` column to `"tag3"` is ignored because `tag` is part of the group key.
- The `with` operator preserves columns not in the group key (`_time` and `_value`).
{{% /note %}}
{{< flex >}}
{{% flex-content %}}
#### Input tables
**Group key:** `tag,_field`
| tag | _field | _time | _value |
|:--- |:------ |:----- | ------:|
| tag1 | foo | 0001 | 1.9 |
| tag1 | foo | 0002 | 2.4 |
| tag1 | foo | 0003 | 2.1 |
| tag | _field | _time | _value |
|:--- |:------ |:----- | ------:|
| tag2 | bar | 0001 | 3.1 |
| tag2 | bar | 0002 | 3.8 |
| tag2 | bar | 0003 | 1.7 |
{{% /flex-content %}}
{{% flex-content %}}
#### Output tables
**Group key:** `tag,_field`
| tag | _field | _time | _value |
|:--- |:------ |:----- | ------:|
| tag1 | foo | 0001 | 1.9 |
| tag1 | foo | 0002 | 2.4 |
| tag1 | foo | 0003 | 2.1 |
| tag | _field | _time | _value |
|:--- |:------ |:----- | ------:|
| tag2 | bar | 0001 | 3.1 |
| tag2 | bar | 0002 | 3.8 |
| tag2 | bar | 0003 | 1.7 |
{{% /flex-content %}}
{{< /flex >}}
---
{{% note %}}
#### Package author and maintainer
**Github:** [@jsternberg](https://github.com/jsternberg)
**InfluxDB Slack:** [@Jonathan Sternberg](https://influxdata.com/slack)
{{% /note %}}
{{< duplicate-oss >}}

2
content/influxdb/cloud/reference/flux/stdlib/monitor/check.md
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@ -82,7 +82,7 @@ from(bucket: "telegraf")
|> range(start: -1h)
|> filter(fn: (r) =>
r._measurement == "disk" and
r._field = "used_percent"
r._field == "used_percent"
)
|> group(columns: ["_measurement"])
|> monitor.check(

1
content/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/aggregates/quantile.md
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@ -53,6 +53,7 @@ _**Data type:** Float_
### method
Defines the method of computation.
Default is `estimate_tdigest`.
_**Data type:** String_

3
content/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/pivot.md
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@ -41,7 +41,8 @@ The output is constructed as follows:
determined from the input tables by the value in `valueColumn` at the row identified by the
`rowKey` values and the new column's label.
If no value is found, the value is set to `null`.
- Any column that is not part of the group key or not specified in the `rowKey`,
`columnKey` and `valueColumn` parameters is dropped.
## Parameters

13
content/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/range.md
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@ -23,7 +23,18 @@ _**Function type:** Transformation_
_**Output data type:* Record_
```js
range(start: -15m, stop: now())
range(
start: -15m,
stop: now()
)
```
#### Behavior of start and stop times
Results include records with `_time` values greater than or equal to the specified `start`
time and less than the specified `stop` time.
```
start <= _time < stop
```
## Parameters

12
content/influxdb/v2.0/reference/flux/stdlib/built-in/transformations/selectors/last.md
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@ -20,7 +20,7 @@ _**Function type:** Selector_
_**Output data type:** Record_
```js
last()
last(column: "_value")
```
{{% warn %}}
@ -28,6 +28,16 @@ last()
`last()` drops empty tables.
{{% /warn %}}
## Parameters
### column
Column used to verify the existence of a value.
If this column is _null_ in the last record, `last()` returns the previous
record with a non-null value.
Default is `"_value"`.
_**Data type:** String_
## Examples
```js
from(bucket:"example-bucket")

6
content/influxdb/v2.0/reference/flux/stdlib/contrib/rows/map.md
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@ -56,7 +56,7 @@ The following example returns the square of each value in the `_value` column:
import "contrib/jsternberg/rows"
data
|> rows.map(fn: (r) ({ _value: r._value * r._value }))
|> rows.map(fn: (r) => ({ _value: r._value * r._value }))
```
{{% note %}}
@ -116,7 +116,7 @@ including those not in the group key, without explicitly remapping them.
import "contrib/jsternberg/rows"
data
|> rows.map(fn: (r) ({ r with _value: r._value * r._value }))
|> rows.map(fn: (r) => ({ r with _value: r._value * r._value }))
```
{{% note %}}
@ -171,7 +171,7 @@ data
import "contrib/jsternberg/rows"
data
|> rows.map(fn: (r) ({ r with tag: "tag3" }))
|> rows.map(fn: (r) => ({ r with tag: "tag3" }))
```
{{% note %}}

2
content/influxdb/v2.0/reference/flux/stdlib/monitor/check.md
View File

@ -82,7 +82,7 @@ from(bucket: "telegraf")
|> range(start: -1h)
|> filter(fn: (r) =>
r._measurement == "disk" and
r._field = "used_percent"
r._field == "used_percent"
)
|> group(columns: ["_measurement"])
|> monitor.check(